Overview

Dataset statistics

Number of variables11
Number of observations117
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory89.1 B

Variable types

Numeric11

Warnings

Yongsan-gu is highly correlated with Gwangjin-gu and 6 other fieldsHigh correlation
Gwangjin-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Seodaemun-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Yeongdeungpo-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Seocho-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
CPI is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
BR is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
HL is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Yongsan-gu is highly correlated with Gwangjin-gu and 6 other fieldsHigh correlation
Gwangjin-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Seodaemun-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Yeongdeungpo-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Seocho-gu is highly correlated with Yongsan-gu and 7 other fieldsHigh correlation
CPI is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
BR is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
ER is highly correlated with Seocho-guHigh correlation
HL is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Yongsan-gu is highly correlated with Gwangjin-gu and 5 other fieldsHigh correlation
Gwangjin-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Seodaemun-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Yeongdeungpo-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
Seocho-gu is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
CPI is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
BR is highly correlated with Gwangjin-gu and 5 other fieldsHigh correlation
HL is highly correlated with Yongsan-gu and 6 other fieldsHigh correlation
ER is highly correlated with CPI and 5 other fieldsHigh correlation
CPI is highly correlated with ER and 9 other fieldsHigh correlation
Gwangjin-gu is highly correlated with CPI and 7 other fieldsHigh correlation
p2 is highly correlated with ER and 9 other fieldsHigh correlation
Yongsan-gu is highly correlated with CPI and 7 other fieldsHigh correlation
HL is highly correlated with ER and 9 other fieldsHigh correlation
CSI is highly correlated with CPI and 4 other fieldsHigh correlation
Seodaemun-gu is highly correlated with ER and 9 other fieldsHigh correlation
BR is highly correlated with ER and 9 other fieldsHigh correlation
Seocho-gu is highly correlated with ER and 8 other fieldsHigh correlation
Yeongdeungpo-gu is highly correlated with CPI and 7 other fieldsHigh correlation
Yeongdeungpo-gu has unique values Unique
Seocho-gu has unique values Unique
HL has unique values Unique

Reproduction

Analysis started2021-12-06 12:59:06.780391
Analysis finished2021-12-06 12:59:19.796653
Duration13.02 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Yongsan-gu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct116
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1010014.88
Minimum748152
Maximum1722671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:19.896608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum748152
5-th percentile750315.4
Q1788864
median833212
Q31366066
95-th percentile1431577.6
Maximum1722671
Range974519
Interquartile range (IQR)577202

Descriptive statistics

Standard deviation286704.1786
Coefficient of variation (CV)0.2838613413
Kurtosis-0.9703167733
Mean1010014.88
Median Absolute Deviation (MAD)79954
Skewness0.757348358
Sum118171741
Variance8.219928605 × 1010
MonotonicityNot monotonic
2021-12-06T21:59:20.035701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7508792
 
1.7%
8023031
 
0.9%
13748161
 
0.9%
14210121
 
0.9%
8190151
 
0.9%
7650001
 
0.9%
13897801
 
0.9%
7854851
 
0.9%
11292951
 
0.9%
13656321
 
0.9%
Other values (106)106
90.6%
ValueCountFrequency (%)
7481521
0.9%
7486061
0.9%
7486671
0.9%
7495301
0.9%
7500301
0.9%
7501211
0.9%
7503641
0.9%
7506521
0.9%
7508792
1.7%
7519391
0.9%
ValueCountFrequency (%)
17226711
0.9%
17070421
0.9%
16916711
0.9%
14450731
0.9%
14393901
0.9%
14347681
0.9%
14307801
0.9%
14273661
0.9%
14210121
0.9%
14169511
0.9%

Gwangjin-gu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct115
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758415.5214
Minimum600697
Maximum1245636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:20.173300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum600697
5-th percentile601978.6
Q1630606
median666576
Q3896553
95-th percentile1053724
Maximum1245636
Range644939
Interquartile range (IQR)265947

Descriptive statistics

Standard deviation170856.3674
Coefficient of variation (CV)0.225280684
Kurtosis0.01172107329
Mean758415.5214
Median Absolute Deviation (MAD)62879
Skewness1.060532378
Sum88734616
Variance2.919189829 × 1010
MonotonicityNot monotonic
2021-12-06T21:59:20.301824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6660912
 
1.7%
6683792
 
1.7%
10322201
 
0.9%
7941971
 
0.9%
9160391
 
0.9%
6124241
 
0.9%
10258661
 
0.9%
8069871
 
0.9%
6549241
 
0.9%
6157581
 
0.9%
Other values (105)105
89.7%
ValueCountFrequency (%)
6006971
0.9%
6008791
0.9%
6010151
0.9%
6012881
0.9%
6015151
0.9%
6017731
0.9%
6020301
0.9%
6021061
0.9%
6024091
0.9%
6025761
0.9%
ValueCountFrequency (%)
12456361
0.9%
12414141
0.9%
12349641
0.9%
10638051
0.9%
10594761
0.9%
10565241
0.9%
10530241
0.9%
10474391
0.9%
10383781
0.9%
10345241
0.9%

Seodaemun-gu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct115
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469225.0513
Minimum354286
Maximum923718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:20.429861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum354286
5-th percentile357631.2
Q1363414
median391286
Q3607619
95-th percentile666736.8
Maximum923718
Range569432
Interquartile range (IQR)244205

Descriptive statistics

Standard deviation137039.1651
Coefficient of variation (CV)0.2920542386
Kurtosis0.9679412703
Mean469225.0513
Median Absolute Deviation (MAD)33029
Skewness1.219018977
Sum54899331
Variance1.877973277 × 1010
MonotonicityNot monotonic
2021-12-06T21:59:20.549529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3612712
 
1.7%
6448912
 
1.7%
6115831
 
0.9%
3577141
 
0.9%
3628141
 
0.9%
3597431
 
0.9%
6103691
 
0.9%
6083211
 
0.9%
3738291
 
0.9%
6147261
 
0.9%
Other values (105)105
89.7%
ValueCountFrequency (%)
3542861
0.9%
3545711
0.9%
3561571
0.9%
3566431
0.9%
3569141
0.9%
3573001
0.9%
3577141
0.9%
3577711
0.9%
3578141
0.9%
3578711
0.9%
ValueCountFrequency (%)
9237181
0.9%
9192181
0.9%
9145061
0.9%
6726521
0.9%
6689351
0.9%
6677281
0.9%
6664891
0.9%
6648591
0.9%
6622611
0.9%
6605001
0.9%

Yeongdeungpo-gu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct117
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean612587.6325
Minimum472469
Maximum1156448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:20.678356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum472469
5-th percentile478137.6
Q1493823
median534660
Q3771306
95-th percentile840372.8
Maximum1156448
Range683979
Interquartile range (IQR)277483

Descriptive statistics

Standard deviation155579.1849
Coefficient of variation (CV)0.2539704961
Kurtosis1.620314279
Mean612587.6325
Median Absolute Deviation (MAD)52756
Skewness1.376499191
Sum71672753
Variance2.420488278 × 1010
MonotonicityNot monotonic
2021-12-06T21:59:20.806739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4938231
 
0.9%
4739581
 
0.9%
4898031
 
0.9%
4921121
 
0.9%
7724351
 
0.9%
5586771
 
0.9%
5341041
 
0.9%
7903611
 
0.9%
4816281
 
0.9%
4903331
 
0.9%
Other values (107)107
91.5%
ValueCountFrequency (%)
4724691
0.9%
4739581
0.9%
4771671
0.9%
4772501
0.9%
4774901
0.9%
4778961
0.9%
4781981
0.9%
4784581
0.9%
4793851
0.9%
4794361
0.9%
ValueCountFrequency (%)
11564481
0.9%
11473571
0.9%
11397131
0.9%
8566951
0.9%
8487801
0.9%
8432201
0.9%
8396611
0.9%
8359831
0.9%
8294241
0.9%
8271101
0.9%

Seocho-gu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct117
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1224937.12
Minimum877917
Maximum2035696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:20.942829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum877917
5-th percentile883555.6
Q1921460
median1059526
Q31527799
95-th percentile1809863.2
Maximum2035696
Range1157779
Interquartile range (IQR)606339

Descriptive statistics

Standard deviation345111.9507
Coefficient of variation (CV)0.2817385033
Kurtosis-0.8670243938
Mean1224937.12
Median Absolute Deviation (MAD)170822
Skewness0.7421564445
Sum143317643
Variance1.191022585 × 1011
MonotonicityNot monotonic
2021-12-06T21:59:21.081272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11066841
 
0.9%
8865191
 
0.9%
8830091
 
0.9%
10299541
 
0.9%
17769661
 
0.9%
9411291
 
0.9%
17713221
 
0.9%
18181921
 
0.9%
13061951
 
0.9%
17501101
 
0.9%
Other values (107)107
91.5%
ValueCountFrequency (%)
8779171
0.9%
8806201
0.9%
8821041
0.9%
8830091
0.9%
8830851
0.9%
8832501
0.9%
8836321
0.9%
8852741
0.9%
8865191
0.9%
8865461
0.9%
ValueCountFrequency (%)
20356961
0.9%
20063811
0.9%
19837141
0.9%
18733901
0.9%
18341991
0.9%
18181921
0.9%
18077811
0.9%
17975211
0.9%
17834111
0.9%
17769661
0.9%

p2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.4358974
Minimum100
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:21.193457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile101
Q1105
median108
Q3110
95-th percentile112
Maximum113
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.516916301
Coefficient of variation (CV)0.03273502046
Kurtosis-0.8040670564
Mean107.4358974
Median Absolute Deviation (MAD)3
Skewness-0.3929520071
Sum12570
Variance12.36870027
MonotonicityNot monotonic
2021-12-06T21:59:21.296112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10519
16.2%
11015
12.8%
11113
11.1%
10912
10.3%
11210
8.5%
1079
7.7%
1068
6.8%
1087
 
6.0%
1027
 
6.0%
1134
 
3.4%
Other values (4)13
11.1%
ValueCountFrequency (%)
1004
 
3.4%
1014
 
3.4%
1027
 
6.0%
1033
 
2.6%
1042
 
1.7%
10519
16.2%
1068
6.8%
1079
7.7%
1087
 
6.0%
10912
10.3%
ValueCountFrequency (%)
1134
 
3.4%
11210
8.5%
11113
11.1%
11015
12.8%
10912
10.3%
1087
 
6.0%
1079
7.7%
1068
6.8%
10519
16.2%
1042
 
1.7%

CPI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.8948718
Minimum96.2
Maximum108.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:21.409480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum96.2
5-th percentile96.68
Q199.3
median101.6
Q3104.9
95-th percentile107.24
Maximum108.8
Range12.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation3.31355227
Coefficient of variation (CV)0.03251932322
Kurtosis-1.168126241
Mean101.8948718
Median Absolute Deviation (MAD)3
Skewness0.04401183517
Sum11921.7
Variance10.97962865
MonotonicityNot monotonic
2021-12-06T21:59:21.781301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.96
 
5.1%
105.55
 
4.3%
100.84
 
3.4%
99.73
 
2.6%
103.43
 
2.6%
104.73
 
2.6%
99.33
 
2.6%
96.63
 
2.6%
100.13
 
2.6%
102.63
 
2.6%
Other values (59)81
69.2%
ValueCountFrequency (%)
96.21
 
0.9%
96.41
 
0.9%
96.51
 
0.9%
96.63
2.6%
96.71
 
0.9%
96.81
 
0.9%
971
 
0.9%
97.21
 
0.9%
97.31
 
0.9%
97.51
 
0.9%
ValueCountFrequency (%)
108.81
0.9%
108.31
0.9%
107.61
0.9%
107.51
0.9%
107.42
1.7%
107.21
0.9%
1071
0.9%
106.51
0.9%
106.21
0.9%
105.82
1.7%

BR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.688034188
Minimum0.5
Maximum3.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:21.900973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q11.25
median1.5
Q32.5
95-th percentile3.05
Maximum3.25
Range2.75
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.7690861565
Coefficient of variation (CV)0.4556105332
Kurtosis-0.6375932648
Mean1.688034188
Median Absolute Deviation (MAD)0.25
Skewness0.3100159763
Sum197.5
Variance0.5914935161
MonotonicityNot monotonic
2021-12-06T21:59:21.985054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.527
23.1%
1.2522
18.8%
0.515
12.8%
2.515
12.8%
1.7511
9.4%
2.757
 
6.0%
3.256
 
5.1%
25
 
4.3%
0.754
 
3.4%
33
 
2.6%
ValueCountFrequency (%)
0.515
12.8%
0.754
 
3.4%
1.2522
18.8%
1.527
23.1%
1.7511
9.4%
25
 
4.3%
2.252
 
1.7%
2.515
12.8%
2.757
 
6.0%
33
 
2.6%
ValueCountFrequency (%)
3.256
 
5.1%
33
 
2.6%
2.757
 
6.0%
2.515
12.8%
2.252
 
1.7%
25
 
4.3%
1.7511
9.4%
1.527
23.1%
1.2522
18.8%
0.754
 
3.4%

ER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct116
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1127.313761
Minimum1019.36
Maximum1228.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:22.097246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1019.36
5-th percentile1054.246
Q11095.13
median1125.9
Q31161.64
95-th percentile1203.132
Maximum1228.67
Range209.31
Interquartile range (IQR)66.51

Descriptive statistics

Standard deviation46.76366208
Coefficient of variation (CV)0.04148238379
Kurtosis-0.273503327
Mean1127.313761
Median Absolute Deviation (MAD)33.1
Skewness-0.08402309608
Sum131895.71
Variance2186.840091
MonotonicityNot monotonic
2021-12-06T21:59:22.240874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125.282
 
1.7%
1171.511
 
0.9%
1131.591
 
0.9%
1183.291
 
0.9%
1062.821
 
0.9%
1111.681
 
0.9%
1132.731
 
0.9%
1105.041
 
0.9%
1125.91
 
0.9%
1184.761
 
0.9%
Other values (106)106
90.6%
ValueCountFrequency (%)
1019.361
0.9%
1019.931
0.9%
1024.991
0.9%
1025.361
0.9%
1033.241
0.9%
1044.551
0.9%
1056.671
0.9%
1060.281
0.9%
1062.821
0.9%
1064.751
0.9%
ValueCountFrequency (%)
1228.671
0.9%
1225.231
0.9%
1220.091
0.9%
1217.351
0.9%
1210.011
0.9%
1208.981
0.9%
1201.671
0.9%
1198.91
0.9%
1197.551
0.9%
1193.791
0.9%

HL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct117
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean890749.1855
Minimum636200.9
Maximum1248662.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:22.366161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum636200.9
5-th percentile647212.9
Q1705225.9
median903488.8
Q31040219.5
95-th percentile1200866.56
Maximum1248662.8
Range612461.9
Interquartile range (IQR)334993.6

Descriptive statistics

Standard deviation186332.5512
Coefficient of variation (CV)0.209186328
Kurtosis-1.259799576
Mean890749.1855
Median Absolute Deviation (MAD)165326.5
Skewness0.1805611281
Sum104217654.7
Variance3.471981965 × 1010
MonotonicityNot monotonic
2021-12-06T21:59:22.490246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
912946.41
 
0.9%
667545.91
 
0.9%
873305.71
 
0.9%
929148.31
 
0.9%
941743.11
 
0.9%
10345351
 
0.9%
690570.91
 
0.9%
8123601
 
0.9%
1149710.31
 
0.9%
7172361
 
0.9%
Other values (107)107
91.5%
ValueCountFrequency (%)
636200.91
0.9%
637123.81
0.9%
637211.61
0.9%
639583.91
0.9%
642741.11
0.9%
645854.91
0.9%
647552.41
0.9%
648512.31
0.9%
649818.91
0.9%
651132.81
0.9%
ValueCountFrequency (%)
1248662.81
0.9%
1240150.71
0.9%
1232276.91
0.9%
1219371.21
0.9%
1216813.41
0.9%
1212641.61
0.9%
1197922.81
0.9%
1190056.71
0.9%
1181802.11
0.9%
1173695.41
0.9%

CSI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.9196581
Minimum91.8
Maximum139.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2021-12-06T21:59:22.620557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum91.8
5-th percentile96.1
Q1111.2
median119.2
Q3128.5
95-th percentile136.84
Maximum139.7
Range47.9
Interquartile range (IQR)17.3

Descriptive statistics

Standard deviation11.83286073
Coefficient of variation (CV)0.09950298306
Kurtosis-0.5419312636
Mean118.9196581
Median Absolute Deviation (MAD)8.7
Skewness-0.3612404601
Sum13913.6
Variance140.016593
MonotonicityNot monotonic
2021-12-06T21:59:22.754032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.83
 
2.6%
117.12
 
1.7%
126.22
 
1.7%
131.92
 
1.7%
106.32
 
1.7%
114.82
 
1.7%
119.22
 
1.7%
127.92
 
1.7%
129.52
 
1.7%
128.82
 
1.7%
Other values (90)96
82.1%
ValueCountFrequency (%)
91.81
0.9%
92.11
0.9%
931
0.9%
93.51
0.9%
93.91
0.9%
94.51
0.9%
96.51
0.9%
98.61
0.9%
102.21
0.9%
102.31
0.9%
ValueCountFrequency (%)
139.71
0.9%
139.41
0.9%
138.91
0.9%
138.71
0.9%
138.11
0.9%
137.81
0.9%
136.61
0.9%
136.21
0.9%
134.11
0.9%
133.91
0.9%

Interactions

2021-12-06T21:59:07.209598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.307963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.408815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.502822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.602637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.702494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.799021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.895108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:07.989415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.079471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.174579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.264702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.361900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.468076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.572573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.678693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.783254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.889182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:08.993280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.098407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.196705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.302298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.400671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.502913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.609360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.709442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.812316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:09.910000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.006200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.106635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.203478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.292309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.388914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.484208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.582727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.687353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.790076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.894490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:10.997879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.099068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.200781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.300306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.394680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.496083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.596169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.689567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.788160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.886498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:11.986643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.088891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.191929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.291880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.387477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.476644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.575473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.670630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:12.765707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.310631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.413116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.514835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.612495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.712219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.810088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.906386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:13.997999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.098686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.197910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.297334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.401018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.498540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.598370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.697757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.797237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.892774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:14.986731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.075787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.172210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.268931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.360254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.459331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.553906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.651750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.744486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.836129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:15.927969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.018374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.104840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.197334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.288016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.369662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.457891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.544691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.631949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.717329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.802920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.889849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:16.971328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.047774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.130953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.213546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.468357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.570821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.665639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.763950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.857819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:17.952795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.046642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.138925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.226395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.319462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.412782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.502156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.596868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.691398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.788203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.880578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:18.973065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:19.066616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:19.158831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:19.243528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-12-06T21:59:19.334112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-12-06T21:59:22.872048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-06T21:59:23.040570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-06T21:59:23.208443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-06T21:59:23.375620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-06T21:59:19.514744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-06T21:59:19.723151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Yongsan-guGwangjin-guSeodaemun-guYeongdeungpo-guSeocho-gup2CPIBRERHLCSI
085215266609137113554184097218310096.23.251145.85636200.9106.3
185131866609136913554125596158310096.53.251123.35637211.6109.9
284697066457636820653750096217010096.63.251125.90637123.8104.5
384575866287936827853578795363510096.63.251135.55639583.9104.0
484543966075836704953466094912010196.73.251154.27642741.1105.0
584387965757636613553027794112910196.63.251165.51645854.993.0
684150065590936449252478793210210196.43.001143.36647552.496.5
783566765431836349251792692754610196.83.001131.69649818.9102.9
882980365143936084951257492511110297.53.001124.78648512.3106.5
982124264753035797850790492286610297.32.751106.93651132.8109.8

Last rows

Yongsan-guGwangjin-guSeodaemun-guYeongdeungpo-guSeocho-gup2CPIBRERHLCSI
107141695110345246605008271101771322105105.70.501095.131173695.4130.8
108142101210383786622618294241783411105106.50.501097.491181802.1129.1
109142736610474396648598359831797521105107.00.501111.721190056.7124.7
110143078010530246664898396611807781105107.20.501131.021197922.8116.6
111143476810565246677288432201818192105107.40.501119.401216813.4117.1
112143939010594766689358487801834199105107.50.501123.281212641.6124.1
113144507310638056726528566951873390105107.40.501121.301219371.2127.9
1141691671123496491450611397131983714106107.60.501143.981232276.9130.7
1151707042124141491921811473572006381106108.30.751160.341240150.7132.9
1161722671124563692371811564482035696106108.80.751169.541248662.8129.5